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Out-of-Domain (OOD) intent detection is vital for practical dialogue systems,and it usually requires considering multi-turn dialogue contexts. However, mostprevious OOD intent detection approaches are limited to single dialogue turns.In this paper, we introduce a context-aware OOD intent detection (Caro)framework to model multi-turn contexts in OOD intent detection tasks.Specifically, we follow the information bottleneck principle to extract robustrepresentations from multi-turn dialogue contexts. Two different views areconstructed for each input sample and the superfluous information not relatedto intent detection is removed using a multi-view information bottleneck loss.Moreover, we also explore utilizing unlabeled data in Caro. A two-stagetraining process is introduced to mine OOD samples from these unlabeled data,and these OOD samples are used to train the resulting model with abootstrapping approach. Comprehensive experiments demonstrate that Caroestablishes state-of-the-art performances on multi-turn OOD detection tasks byimproving the F1-OOD score of over $29\%$ compared to the previous best method.

Out-of-Domain Intent Detection Considering Multi-turn Dialogue Contexts